Optimizing multi-sensor deployment via ensemble pruning for wearable activity recognition

With the rapid development of sensor types and processers, most wearable activity recognition systems tend to making use of multiple homogeneous or heterogeneous sensors to obtain plethora information. However, in a realistic environment, it is difficult to configure an appropriate multi-sensor deployment to gain a tradeoff among computational complexity, accuracy and subject personality. In this paper, a multi-sensor fusion with ensemble pruning system (MSF-EP) is designed to connect with multi-sensor based wearable activity recognition system. As a result, the multi-sensor configuration problem is transformed to multiple ensemble classifier pruning problem. With respect to ensemble pruning for MSF-EP system, two popular order-based ensemble pruning approaches are utilized firstly: reduce-error pruning (RE) and complementarily pruning (Comp). Then, in light of the proposed MSF-EP system, two new ensemble pruning criteria are proposed: mRMR pruning and discriminative pruning (Disc). The mRMR pruning measure is based on mutual information and is a composite criterion of classifier redundancy and relevance with regard to the selected classifier set. Taking into account the discriminations of misclassified instances and correctly classified instances in classifier subset selected respectively, another ensemble pruning measure Disc, which combining RE pruning and Comp pruning, is presented in the form of mutual information also. Finally, the proposed pruning learner with lower error is selected as final ensemble classifier. Through the algorithm, the number and type of multi-sensor are appropriately decided to optimize the multi-sensor fusion without regrading the accuracy performance. The system conducts experimental studies on two real-world activity recognition data sets and the results show the superiority of system over other ensemble algorithms.